Dr. Kaoutar
El Maghraoui
Principal Research Scientist & Manager at IBM Research. Adjunct Professor at Columbia University. ACM Distinguished Speaker & Member. Pioneering AI hardware-software co-design for the next generation of intelligent systems.
Shaping the Future of AI
Two decades of pioneering research at the intersection of AI, hardware, and distributed systems.
Dr. Kaoutar El Maghraoui is a Principal Research Scientist and Manager at IBM Research's AI Hardware Center, where she leads cross-functional teams developing AI model enablement and hardware-software co-design for IBM's next-generation AI accelerators.
She is also an Adjunct Professor at Columbia University, teaching High-Performance Machine Learning and Scalable Large Language Models. Her research spans AI hardware-software co-design, neural architecture search, analog in-memory computing, and LLM optimization.
Recognized as an ACM Distinguished Member (top 10% worldwide) and ACM Distinguished Speaker, she has delivered over 60 keynotes and invited talks at major international conferences. She holds 17 US patents and has published extensively in top-tier venues including Nature Communications, ICLR, NeurIPS, ASPLOS, and ICML.
Current Roles
Principal Research Scientist & Manager
IBM Research AI Hardware Center
Adjunct Professor
Columbia University
ACM Distinguished Speaker
Association for Computing Machinery
Science Advisory Board Member
University at Albany — Emerging AI Systems
Advancing AI at Every Layer
From silicon to software — building the foundations for the next era of artificial intelligence.
AI Hardware-Software Co-Design
Leading the development of optimized AI model mapping and deployment strategies for next-generation AI accelerators, bridging the gap between algorithm design and hardware capabilities.
Analog In-Memory Computing
Pioneering neural architecture search techniques for analog in-memory computing, enabling energy-efficient AI inference through novel hardware paradigms.
LLM Optimization & Inference
Developing dynamic KV cache management and efficient inference techniques for large language models, accelerating enterprise AI deployment at scale.
Neural Architecture Search
Creating multi-objective hardware-aware NAS frameworks that automatically discover optimal neural network architectures for specific hardware targets.
Supernetwork-based efficient mapping of deep learning models
Key Research Publications
Selected high-impact publications spanning AI hardware co-design, neural architecture search, and model optimization.
Ultra-Low Precision 4-bit Training of Deep Neural Networks
Novel techniques and numerical representation formats to scale the precision of training systems from 8-bits to 4-bits, introducing adaptive Gradient Scaling for quantized gradients.
X Sun, N Wang, CY Chen, J Ni, A Agrawal, X Cui, S Venkataramani, K El Maghraoui, et al.
A Flexible and Fast PyTorch Toolkit for Simulating Training and Inference on Analog Crossbar Arrays
A comprehensive PyTorch toolkit enabling efficient simulation of analog in-memory computing for neural network training and inference on crossbar arrays.
MJ Rasch, D Moreda, T Gokmen, M Le Gallo, F Carta, C Goldberg, K El Maghraoui, et al.
A Comprehensive Survey on Hardware-Aware Neural Architecture Search
An extensive survey and taxonomy of hardware-aware NAS methods, covering search strategies, hardware metrics, and deployment considerations across diverse platforms.
H Benmeziane, K El Maghraoui, H Ouarnoughi, S Niar, M Wistuba, et al.
ModelOps: Cloud-Based Lifecycle Management for Reliable and Trusted AI
A framework for managing the full lifecycle of AI models in the cloud, addressing reliability, trust, and operational efficiency for enterprise AI deployments.
W Hummer, V Muthusamy, T Rausch, P Dube, K El Maghraoui, A Murthi, et al.
Using the IBM Analog In-Memory Hardware Acceleration Kit
Comprehensive guide to the IBM AIHWKit for neural network training and inference on analog in-memory computing hardware, enabling energy-efficient AI acceleration.
M Le Gallo, C Lammie, J Büchel, F Carta, O Fagbohungbe, C Mackin, K El Maghraoui, et al.
Neural Architecture Search for In-Memory Computing-Based Deep Learning Accelerators
A review of NAS techniques tailored for in-memory computing accelerators, bridging the gap between neural network design and emerging hardware paradigms.
O Krestinskaya, ME Fouda, H Benmeziane, K El Maghraoui, A Sebastian, et al.
Deep Compression of Pre-trained Transformer Models
Techniques for dramatically compressing pre-trained transformer models while maintaining accuracy, enabling efficient deployment on resource-constrained hardware.
N Wang, CCC Liu, S Venkataramani, S Sen, CY Chen, K El Maghraoui, et al.
Multi-Objective Hardware-Aware Neural Architecture Search with Pareto Rank-Preserving Surrogate Models
A novel multi-objective NAS framework using Pareto rank-preserving surrogate models to efficiently discover optimal architectures across multiple hardware constraints.
H Benmeziane, H Ouarnoughi, K El Maghraoui, S Niar
Shaping the Next Generation
As Adjunct Professor at Columbia University, I bridge cutting-edge IBM Research with graduate education — equipping students to design, optimize, and deploy AI systems at scale.
Teaching Philosophy
Systems Thinking
Understanding how components interact at scale — from silicon to software stack. Students don't just learn algorithms; they implement them on real hardware and measure the results.
Empirical Rigor
Every claim must be measured and validated. Courses emphasize profiling, benchmarking, and performance analysis as first-class skills alongside theoretical foundations.
Ethical Awareness
Considering the societal implications of AI systems. The energy cost of training, the accessibility of deployment, and the responsibility of building technology that serves everyone.
Institution
Columbia University
Courses
2 Graduate
Focus
AI Systems & HPC
Approach
Research-Driven
In the Classroom
Teaching GPU Architecture at Columbia University
Lecture on Model Pruning — HPML at Columbia
Guest Lecture at University of Sharjah
High-Performance Machine Learning
COMS E6998 · Columbia University
At the intersection of AI and High-Performance Computing, this course covers foundational and advanced techniques that drive efficient AI systems — from GPU programming and distributed training to LLM serving and model compression. Based on PyTorch and CUDA.
Scaling LLMs: Systems, Optimization & Emerging Paradigms
COMS E6998 · Columbia University
A frontier research seminar exploring scaling, optimizing, and deploying large language models through a structured progression from foundations to futures. Students present and critique top-tier papers (NeurIPS, ICML, ICLR, ISCA, ACL) and produce a survey paper with experimental evaluation.
Distinguished Speaker
Over 60 keynotes and invited talks at major international conferences, inspiring audiences worldwide on AI, technology, and innovation.
Agentic AI: From Creation to Collaboration
Women in AI Morocco / INPT Workshop
Agentic AI: From Creation to Collaboration
Women in AI Morocco / INPT Workshop
The Next Wave: Reinventing Intelligence and Compute Architecture
Women in AI Morocco Summit 2025
Agentic AI Workshop — Full Auditorium
Women in AI Morocco / INPT
The Future of AI — An IBM Research Perspective
IBM TechXChange
Revolutionizing Enterprise AI: The Power and Promise of Foundation Models
Women in Research Webinar Series (QUWA) — University of Sharjah
Revolutionizing Enterprise AI
IEEE Services Conference
Scaling Foundation Models for Enterprise
MoroccoAI / Al Akhawayn University
Foundation Models at Scale
AI Seminar Series — Alfaisal University
Women in Computing: Breaking Boundaries
ArabWIC Conference
AI for Business: A Unique Set of Challenges
Women in Data Science @ Stanford / KACST
Watch Keynotes
Selected keynote recordings from major conferences and events.
Powering the Future of AI through Specialized Hardware
Keynote at MoroccoAI Annual Conference discussing how specialized AI hardware accelerators are essential for sustainable and efficient AI, covering analog in-memory computing and hardware-software co-design strategies.
Accelerating, Optimizing, and Automating AI across the Stack
A comprehensive keynote on the challenges of deploying complex AI models efficiently, covering optimization techniques from hardware to software, and automated approaches to neural architecture search.
Platform for Next Generation Analog AI Hardware Acceleration
Presentation at the tinyML On Device Learning Forum on building platforms for next-generation analog AI hardware, enabling efficient on-device inference through novel computing paradigms.
Women in Services Computing — Award & Keynote
Award acceptance speech and presentation at the IEEE International Symposium on Women in Services Computing, highlighting contributions to AI research and inspiring the next generation of women in technology.
On the Airwaves
Regular contributor to IBM's Mixture of Experts podcast, discussing the latest trends in AI hardware, model optimization, and the future of intelligent systems.
IBM Mixture of Experts
A weekly podcast where IBM researchers break down the latest in AI, technology, and innovation.
AI Year in Review: Trends Shaping 2026
Kaoutar unpacks the AI hardware supply crisis and NVIDIA's chip dominance, while Gabe Goodhart defends open source's breakout year with models challenging proprietary systems.
Mainframe Modernization: COBOL and AI
Diving into AI-powered mainframe modernization, exploring how modern AI techniques can transform legacy COBOL systems and accelerate enterprise digital transformation.
AI Hardware Model Optimization
Exploring the intricacies of AI hardware with deep dives into model optimization techniques, chip architectures, and the future of specialized AI accelerators.
Manus, Vibe Coding, Scaling Laws & Perplexity's AI Phone
Episode 46 discussing the latest AI agent developments, the emergence of vibe coding, scaling law debates, and Perplexity's ambitious AI phone project.
Your Brain on ChatGPT & Human-like AI for Safer AVs
Discussing the cognitive impact of LLMs on the human brain, the evolution of autonomous vehicles with human-like AI systems, and the rise of AI-generated advertising.
Apple's WWDC, Meta & Scale AI, o3-pro
Analyzing Apple's WWDC announcements, Meta's partnership with Scale AI, the capabilities of o3-pro, and the latest in fault-tolerant quantum computing.
Media & Coverage
Expert commentary, profiles, and features in leading technology publications and organizations worldwide.
Featured In
Featured Press Clippings
TelQuel Impact — Puts IBM on the AI Radar & 20 Leadership Perspectives
From IBM's research centers in the United States, Kaoutar El Maghraoui is one of the rising figures in the global race for artificial intelligence. Featured among 20 distinguished Al Akhawayn University alumni shaping the future.
Les équipes diversifiées produisent des solutions plus robustes, créatives et équitables
An in-depth interview on AI infrastructure, the IBM Spyre accelerator, hardware-software co-design, and the importance of diverse teams in shaping AI's future.
All eyes on AI at Apple WWDC: Can slow and steady still win the race?
Apple made a significant leap, with the introduction of Apple Intelligence, which combines intelligent systems and personalized contexts.
Custom chips drive AI's future
Reducing dependence on NVIDIA merely shifts the center of power from one giant to another.
Anthropic's microscope cracks open the AI black box
What Anthropic is doing is fascinating... They're starting to show that models develop internal reasoning structures that look a lot like associative memory.
GHC Program Co-Chair — Kaoutar El Maghraoui
Recognized for leadership in the Grace Hopper Celebration, the world's largest gathering of women technologists, and co-founding Arab Women in Computing.
AI's Evolution: From Symbolic Representations to Generative Intelligence
Featured as a distinguished speaker, sharing insights on the evolution of AI from symbolic systems to modern generative intelligence.
AI Year in Review: Trends Shaping 2026
Highlights diminishing returns of pure compute scaling, urging efficiency in training and inference — core to IBM's AI Hardware Center focus.
Women in AI — La Nouvelle Tribune Special Issue
Featured in a special issue celebrating women leaders in artificial intelligence, highlighting contributions to AI hardware and systems research.
Canal Atlas Interview
Media coverage on AI research and innovation
Awards & Honors
Recognized by leading institutions for contributions to AI research, open-source innovation, and service to the computing community.
Breaking Boundaries
Grace Hopper Celebration
IBM Outstanding Technical Achievement Award
IBM Research
PyTorch, vLLM, CI/CD contributions
ACM Distinguished Speaker
Association for Computing Machinery
Selected for global speaking program
IEEE Open-Source Science Award
IEEE
Analog In-Memory Hardware Acceleration
IBM Outstanding Technical Achievement Award
IBM Research
Analog AI Toolkits
ACM Distinguished Member
Association for Computing Machinery
Top 10% of ACM members worldwide
IBM Technical Corporate Award
IBM
One of only 38 IBM researchers selected
IEEE TCSVC Women in Service Computing Award
IEEE
Outstanding contributions to service computing
Best Research Award
4th Forum for Women in Research, UAE
Recognition for research excellence
Let's Connect
Interested in collaboration, speaking engagements, or research partnerships? I'd love to hear from you.
IBM T.J. Watson Research Center
Yorktown Heights, NY 10598
Speaking Inquiries
I am available for keynotes, panel discussions, and workshops on AI, hardware-software co-design, and technology leadership. With 60+ keynotes delivered across 4 continents, I bring deep expertise and engaging delivery to every event.
Research Collaboration
I welcome collaborations with academic institutions and industry partners on AI systems research, hardware-software co-design, and efficient AI deployment. I also supervise graduate students and postdoctoral researchers at Columbia University.